Camera resectioning

Lens (geology)

Camera lens

Checkerboard

Mathematics

Camera auto-calibration

Gene

Image (mathematics)

Statistics

Engineering

Pixel

Calibration

Artificial intelligence

Chemistry

Geometry

Corner detection

Computer science

Petroleum engineering

Computer vision

Biochemistry

Robustness (evolution)

Frontiers in Physics

Camera calibration plays an important role in various optical measurement and computer vision applications. Accurate calibration parameters of a camera can give a better performance. The key step to camera calibration is to robustly detect feature points (typically in the form of checkerboard corners) in the images captured by the camera. This paper proposes a robust checkerboard corner detection method for camera calibration based on improved YOLOX deep learning network and Harris algorithm. To get high checkerboard corner detection robustness against the images with poor quality (i.e., degradation, including focal blur, heavy noise, extreme poses, and large lens distortions), we first detect the corner candidate areas through the improved YOLOX network which attention mechanism is added. Then, the Harris algorithm is performed on these areas to detect sub-pixel corner points. The proposed method is not only more accurate than the existing methods, but also robust against the types of degradation. The experimental results on different datasets demonstrate its superior robustness, accuracy, and wide effectiveness.

A Robust Checkerboard Corner Detection Method for Camera Calibration Based on Improved YOLOX